Method and system for conveying data from monitored scene via surveillance cameras

ABSTRACT

A system and method for monitoring sensors via surveillance cameras is disclosed. In the system, surveillance cameras generating image data of scenes, where the sensors are included in the scenes. The sensors detect information concerning the scenes and encode the information so that it can be derived from the image data. Preferably, the sensors encode the information concerning the scenes by modulating a visible light source, the signals of which are captured within the image data by the cameras. An analytics system then analyzes the image data and decodes the information from the sensors encoded within the image data. Preferably, an integrated analytics system of the cameras executes the analysis. Exemplary sensors include sensors for detecting temperature, moisture, audio/voice, and product selection in a retail premises.

RELATED APPLICATIONS

This application is related to:

U.S. application Ser. No. ______ filed on an even date herewith,entitled “Method and system for surveillance camera arbitration ofuplink consumption,” attorney docket number 0340.0001US1, now U.S.Patent Publication No. ______;

U.S. application Ser. No. ______ filed on an even date herewith,entitled “Method and system for pooled local storage by surveillancecameras,” attorney docket number 0340.0002US1, now U.S. PatentPublication No. ______;

U.S. application Ser. No. ______ filed on an even date herewith,entitled “System and method for designating surveillance camera regionsof interest,” attorney docket number 0340.0003US1, now U.S. PatentPublication No. ______;

U.S. application Ser. No. ______ filed on an even date herewith,entitled “System and method for deadzone detection in surveillancecamera network,” attorney docket number 0340.0004US1, now U.S. PatentPublication No. ______;

UU.S. application Ser. No. ______ filed on an even date herewith,entitled “System and method for overlap detection in surveillance cameranetwork,” attorney docket number 0340.0005US1, now U.S. PatentPublication No. ______;

U.S. application Ser. No. ______ filed on an even date herewith,entitled “System and method for retail customer tracking in surveillancecamera network”, attorney docket number 0340.0006US1., now U.S. PatentPublication No. ______;

U.S. application Ser. No. ______ filed on an even date herewith,entitled “Method and system for modeling image of interest to users,”attorney docket number 0340.0007US1, now U.S. Patent Publication No.______;

U.S. application Ser. No. ______ filed on an even date herewith,entitled “System and method for using mobile device of zone andcorrelated motion detection,” attorney docket number 0340.0008US1, nowU.S. Patent Publication No. ______;

U.S. application Ser. No. ______ filed on an even date herewith,entitled “System and method for configuring surveillance cameras usingmobile computing devices,” attorney docket number 0340,0010US1, now U.S.Patent Publication No. ______;

and

U.S. application Ser. No. ______ filed on an even date herewith,entitled “System and method for controlling surveillance cameras,”attorney docket number 0340.0011US1, now U.S. Patent Publication No.______:

All of the afore-mentioned applications are incorporated herein by thisreference in their entirety.

BACKGROUND OF THE INVENTION

Video surveillance systems are often deployed in schools, governmentbuildings, small businesses, retail stores and corporate offices, andeven many residences. These surveillance systems are typically comprisedof surveillance cameras that capture image data, image data storagesystems that store the image data along with possibly metadata, andincreasingly analytics systems that analyze the image data and possiblygenerate the metadata.

The analytics systems are becoming increasingly powerful. Often, theanalytics systems will track moving objects against fixed backgroundmodels. More sophisticated functions include object detection todetermine the presence of an object or classify the type of object orevent. The analytics systems generate video primitives or metadata forthe detected objects and events, which the analytics systems can furtherprocess or send over the data networks to other systems for storage andincorporation into the image data as metadata, for example.

While analytics systems have historically been separate systems apartfrom the surveillance cameras, the surveillance cameras themselves areincreasingly providing this functionality. Integrating the analyticsfunctionality within the cameras themselves has advantages. Iteliminates the cost and maintenance associated with deploying a separateanalytics system to accomplish the same objective, and enables moreefficient analysis by eliminating the messaging overhead associated withsending the image data over the data network for analysis by theseparate analytics systems.

SUMMARY OF THE INVENTION

With the increasing power of analytics systems, there are trends tointegrate surveillance camera systems into larger systems associatedwith security and business functions. Nevertheless, it becomes difficultto propagate information between the different systems to fulfillpossibly higher level functions. Moreover, it is always a challenge todeploy added sensing capabilities that may provide additional data thatwould improve the analytics powers of the systems and contributeinformation to the video analytics systems. Typically, differentheterogeneous sensors must be located at different places in thepremises. Moreover, data and power connections must typically beprovided to those sensors.

In general, according to one aspect, the invention features a system formonitoring sensors. This system comprises surveillance camerasgenerating image data of scenes. Then, sensors are distributed in thosescenes to detect information concerning the scenes. This information isthen encoded so that it can be derived from the image data. An analyticssystem can then analyze the image data, and decode the information fromthe sensors.

In one example, the sensors can detect environmental information andthen encode that environmental information. Examples include sensorsthat detect moisture or water, or temperature.

In other examples, the sensors detect security information and thenencode that security information. One example here is that a sensorcould monitor sounds for voice patterns characteristic of aggression.The sensors could then generate an alert that could be decoded by theanalytics system.

It still other examples, the sensors could detect business informationand then encode that business information. Here, a sensor could bedeployed in the scene in order to detect merchandise and possibly thenumber of items on a shelf. This information could then be provided toand derived by the analytics system.

In one example, an image data storage system could be provided forstoring the image data. The analytics system would then generate metadata from the decoded information and then store that metadata with theimage data in the image data storage system.

There are a number of different ways of implementing the sensors. Forexample, the sensors could comprise modulated light source or sourcesthat would encode the information by modulating light from the sources.The modulated light could even be generated in the infrared spectrum,outside the visible range.

In order to ensure robust communication of the information, themodulated light source should be modulated at less than a frame rate ofthe surveillance cameras. This will help to prevent information loss.

In the example of a hybrid system, a security control system could beprovided that receives the decoded sensor information and generatessecurity alarms. In other examples, the system could comprise a businesslogic system that receives the decoded sensor information and updatesproduct availability information. In still another example, the systemcould further comprise an environmental control system that receives thedecoded sensor information and controls environmental systems.

In general, according to another aspect, the invention features amonitoring method. This method comprises generating image data of scenesand detecting information concerning the scenes and encoding theinformation so that it can be derived from the image data. The imagedata is then analyzed and the information decoded.

In general, according to still another aspect, the invention features amethod for analyzing image data from a surveillance camera. This methodcomprises installing mechanisms for generating predetermined opticalpatterns in response to events of interest in a scene monitored by thesurveillance camera. The image data is then monitored for thepredetermined optical patterns. Finally, metadata for the image data canbe generated in response to detecting the optical patterns.

The above and other features of the invention including various noveldetails of construction and combinations of parts, and other advantages,will now be more particularly described with reference to theaccompanying drawings and pointed out in the claims. It will beunderstood that the particular method and device embodying the inventionare shown by way of illustration and not as a limitation of theinvention. The principles and features of this invention may be employedin various and numerous embodiments without departing from the scope ofthe invention.

BRIEF DESCRIPTION OF THE DRAWINGS

In the accompanying drawings, reference characters refer to the sameparts throughout the different views. The drawings are not necessarilyto scale; emphasis has instead been placed upon illustrating theprinciples of the invention. Of the drawings:

FIG. 1 is a schematic drawing showing a network of surveillance camerasincluding in-scene sensors;

FIG. 2 is a schematic drawing showing a network of surveillance camerasincluding in-scene sensors in a retail environment;

FIG. 3 is a schematic diagram showing the components of the surveillancecameras including an integrated analytics system and image data storagesystem;

FIGS. 4A and 4B are schematic diagrams showing two examples of thein-scene sensors, where FIG. 4A shows an in-scene sensor with a fourelement array of signaling optical elements such as Light EmittingDiodes (LEDs) and FIG. 4B shows an in-scene sensor with a singlesignaling optical element (LED); and

FIGS. 5A-5E are flow diagrams showing the processing of image data ofthe scenes and the decoding and targeting of the data from the in-scenesensors, where FIG. 5A shows the use of temperature signals; FIG. 5Bshows the use of door signals; FIG. 5C shows the use of water/humiditysignals; FIG. 5D shows the use of merchandise signals; and FIG. 5E showsthe use of audio signals.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The invention now will be described more fully hereinafter withreference to the accompanying drawings, in which illustrativeembodiments of the invention are shown. This invention may, however, beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein; rather, these embodiments areprovided so that this disclosure will be thorough and complete, and willfully convey the scope of the invention to those skilled in the art.

As used herein, the term “and/or” includes any and all combinations ofone or more of the associated listed items. Further, the singular formsincluding the articles “a”, “an” and “the” are intended to include theplural forms as well, unless expressly stated otherwise. It will befurther understood that the terms: includes, comprises, including and/orcomprising, when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof. Further, it will be understood that when anelement, including component or subsystem, is referred to and/or shownas being connected or coupled to another element, it can be directlyconnected or coupled to the other element or intervening elements may bepresent.

FIG. 1 shows an exemplary system 10 comprising surveillance cameras andsensors constructed according to the principles of the presentinvention.

The system 10 includes surveillance cameras 103 installed at a premises52 of an organization. The surveillance cameras 103 generate image data250 and communicate with each other and with other security devices overa local (enterprise) network 210, which may be wired, wireless, or ahybrid of wired and wireless links.

A number of approaches may be employed in the alternative or in a hybridfashion to store the image data 250 generated by the surveillancecameras 103-1, 103-2. A local image data storage system 212 is shown,deployed on the local network 210. In other examples, each or some ofthe cameras 103 includes a camera image data storage system 174.Further, streams of image data 250 can be transferred over a networkcloud 50 to a cloud or remote image data storage system 310.

The image data 250 can then be accessed over the network cloud 50 byuser mobile computing devices such as smartphones, tablet computingdevices, laptop computer running operating systems such as Windows,Android, Linux, or IOS, in examples.

A specific example showing how the cameras might be deployed isillustrated. Within the premises 52, camera1 103-1 focuses upon a door62. Camera2 103-2 detects motion of individuals 60 such as customersand/or sales personnel near a point of sale terminal (e.g. cash registeron a table 68, to illustrate one specific example.

Other components of the system 10 are the video analytics systems. Acloud video analytics system is shown 312 that receives the image datafrom the surveillance cameras 103 via the network cloud 50. Here, thecloud video analytics system 312 might be managed by a third partyhosting company and is presented to the enterprise local network 210 asa single virtual entity, in one example. In other examples, theanalytics system is installed on the local network 210 and may be ownedby the same business entity as the surveillance camera system 10.Further, a camera analytics system 176 integrated within one or more ofthe surveillance cameras 103 is another option.

Also shown are various types of in-scene sensors that can be deployed inand around the premises 52. In the specific example shown, a temperaturesensor/thermostat 454, a door sensor 462, a water/humidity sensor 452,an audio/voice sensor 456, and a cash register sensor 76 are provided.The cash register sensor 76 emits a light beam 84 when the drawer 74 isopened. Also shown are the various systems that can be targeted with thesensor information such as meta data 160 in the cloud image data storagesystem 310, business logic system 240 for logistics or analytics, asecurity control system 238 or an environmental control system 242.

In the example, the temperature sensor/thermostat 454 detects theambient temperature and possibly other environmental quantities such ashumidity. It is used to control heating and/or air conditioning unitsvia and the environmental control system 242.

The door sensor 462, on the other hand, detects the state of the door62. Specifically, it monitors whether the door 62 is opened or closed.In other examples, the door sensor 462 further detects the movement ofindividuals relative to the door such as detecting when individuals passthe threshold of the door 62.

A water/humidity sensor 452 is used to detect water on the floor of thepremises 52, in one example. Specifically, it includes an integratedmoisture detector 464 that tests for water on the floor of the premises52, in the illustrated example. In another example, the detector 464 isa humidity sensor (e.g. hygrometer) that senses relative humidity in theair within the premises 52.

The audio/voice sensor 456 monitors for audible noises in the area ofthe point of sale terminal 72 in the illustrated example. Theaudio/voice sensor 456 includes a microphone 458 for this purpose. inone example, the audio/voice sensor 456 monitors speech for speechpatterns that are that are indicative of aggression or fear. In thisway, it can be used to detect whether or not there is a robbery going onor whether a supervisor needs to be summoned in order to handle an iratecustomer. In other examples, the audio/voice sensor 456 can detectsounds indicative of potential safety threats to the premises 52 and itsoccupants, such as shattering of glass, and noise that exceedspredetermined threshold levels. In yet another example, the audio/voicesensor 456 can sense sounds that are below the range of human hearingthat travel long distances, such as infrasonic sounds for detectingearthquakes,

Also shown is a cash register sensor 76. It detects the status of thecash drawer 74 in this example. Specifically, the sensor 76 determineswhether possibly money is contained in the cash drawer 74 or whether ornot the cash drawer is opened or closed, in examples. In response to thedetermined conditions, the light beam 84 can be modulated at differentfrequencies and/or duty cycles corresponding to each of the determinedconditions.

Each of the sensor units 454, 462, 452, 456, and 76 include visiblecommunication units 450. These units 450 generate visible informationthat is at least visible to the surveillance cameras 103. This visiblecommunication may also take place in optical bands that are visible tohumans. In another example, the visible communication could also takeplace in infrared wavelengths, for example, that are outside the visiblebands. In this example, surveillance cameras 103 equipped with imagesensor that are sensitive to the infrared and can receive the visibleinformation sent from the sensors over infrared wavelengths.

In any event, these visible communication units 450 are either directlyor indirectly visible by the surveillance cameras 103 and typicallylocated within the fields of view 105-1, 105-2 of the surveillancecameras. The information generated by the sensor units 454, 462, 452,456, and 76 is then encoded and included within image data 250 of thescene captured by the surveillance cameras 103. In this way, theinformation from the sensors can be derived from the image data 250. Asa result, one or more of the analytics systems 174, 310, for example,can analyze the image data 250 and decode the information from thesensors within the image data 250. This information can then be madeavailable to the security control system 238, the environmental controlsystem 242, and a business logic system 240, in examples.

A number of advantages arise from such a system. Specifically, dataconnections do not need to be made to any of the sensor units 454, 462,452, 456, and 76. Because the sensors do not require connections toother components on the local network 210 to communicate the informationfrom the sensors, the sensors can be designed without traditional datanetworking hardware and/or software protocol interfaces for transmittingthe sensor information, which decreases complexity and cost and easesdeployment. The data connections are instead implemented in thedetection and analysis of the image data 250 from the surveillancecameras. Moreover, information from the sensors is further madeavailable to the video analytics system. This allows for the informationfrom the sensors to be used in the analytics system. Specifically, metadata 160 can be added to the image data 250 as it is stored in an imagedata storage system 310, 176. Moreover, the sensor information can nowbe distributed to the other heterogeneous systems that monitor orcontrol the premises 52.

FIG. 2 illustrates another example where the system 10 is deployed at aretail environment. In general, this figure is intended to show how thein-scene sensors can also be deployed to assist in the tracking ofmerchandise using merchandise sensors 460-1, 460-2. Here, the sensorinformation would generally be provided to the business logic system240. Also illustrated here is the example where the analytics system andimage data storage would be integrated into the cameras. Specifically,product sensors 460 are provided that monitor the status of products ormerchandise located on shelves 98 within a retail establishment, forexample.

Here, a surveillance camera 103-4-4 monitors product sensors 460-1 and460-2. These products sensors 460 detect the number of boxes on theshelves 98 for different products. As a result, when a consumer 60-1removes a product 92-8, this event can be detected by product sensor460-1. Its communication unit 450 then visually encodes removal of theproduct, which the surveillance cameras 103 capture in image data 250.The analytics system 174 then decodes the image data 250 from thesurveillance camera 103-1 to obtain the product removal information. Theproduct removal information can then be made available to a businesslogic system 240 that maintains the inventory levels for the store. Onthe other hand, this information can also be provided to a securitycontrol system 238 to possibly notify sales personnel or security guardsthat a product has been removed from the shelves.

FIG. 3 shows some of the components of an exemplary surveillance camera103. In the example, the surveillance camera 103 stores its image data250 locally and includes an integrated analytics system (here, a cameraanalytics system 176) as discussed herein above, for one embodiment.

The camera 103 includes a processing unit (CPU) 138, an imager 140, acamera image data storage system 174 and a network interface 142. Anoperating system 136 runs on top of the CPU 138. A number of processesor applications are executed by the operating system 136. The processesinclude a control process 162 and a camera analytics system 176.

The surveillance camera 103 saves image data 250 captured by the imager140 to the camera image data storage system 174. Each camera 103 cansupport one or more streams of image data 250. The control process 162receives and sends messages 264 via the network interface 142. Thecontrol process 162 also stores image data 250 and meta data 160 to thecamera image data storage system 174.

The control process 162 sends the image data 250 to the integratedcamera analytics system 176 for analysis in some cases. The cameraanalytics system 176 analyzes the image data 250 and generates meta data160 in response to the analysis. The meta data 160 can also be stored tothe camera image data storage system 174.

In some cases, the cameras 103 may also or alternatively stream imagedata to the user device 400 or the external analytics system 312 andthese analytics systems then analyze the image data 250.

FIG. 4A illustrates one example of a generic sensor 466 with its visiblecommunication unit 450. In this example, the visible communication unit450 comprises an array of 4 light emitting diodes (LEDs), for example.The light emitting diodes could generate light in the visible or nonvisible (to humans) regions of the infrared. Nevertheless, thesurveillance cameras are able to detect the light from the diode array.The generic sensor 466 further includes a sensor controller 480 and atransducer 482 for detecting some characteristic of the surroundingenvironment. This transducer could be a microphone for detecting soundincluding noises indicative of aggressive speech, a moisture sensor fordetecting water, and/or a temperature sensor for detecting temperatureto list a few examples.

The information from the transducer 482 is accessed by the sensorcontroller 480. The sensor controller 480 then encodes the informationthrough the modulation of the array of light emitting diodes. In anotherexample, rather than light emitting diodes, light shutters or LiquidCrystal Displays (LCD) could be used. In any event, the controller 480typically modulates the communications unit 450 at a modulationfrequency that is less than the frame rate of the surveillance cameras103. This minimizes the potential of data loss. Preferably, themodulation frequency of the communications unit 450 is less than halfthe frame rate of the surveillance cameras 103 to ensure robustcommunication of the sensor information with little or no lostinformation.

FIG. 4B shows another example of the sensor 466. This example includesonly a single element communications unit 450. This could be a singlelight emitting diode, light shutter unit, or LCD display, for example.

FIG. 5A through 5E are flow diagrams intended to illustrate a number ofdifferent uses for in-scene sensors and how the information from thesensors is used. In each of the flow diagrams, an analytics system 312,176 receives image data 250 captured from the surveillance cameras 103and extracts information from the in-scene sensors encoded within theimage data. 250. The analytics system 312,176 then stores the extractedinformation from the sensors as meta data 160, saves the meta data 160along with the meta data 160, and provides the meta data 160 for furtherprocessing by other components in the system in accordance withsensor-specific information associated with the meta data 160.

In FIG. 5A, the image data is analyzed from the surveillance cameras instep 710. An in-field optical signal from a temperature sensor 466 hasbeen detected in step 712. The information from the in-field temperaturesensor is decoded in step 714. In step 716, the temperature is stored asmeta data 160 with the image data 250 in an image data storage system174, 310. Finally, in step 718, this temperature information is passedto an environmental control system 242 and a temperature log is updatedwith the temperature information. In other examples, the temperatureinformation is possibly stored in a log file within the image datastorage system 174,310 or used as part of the control algorithm for thatenvironmental control system 242.

FIG. 5B shows a similar process in which an in-field door sensor 462 ismonitored and detected in step 720. The information from the door sensor462 is decoded in step 722. Meta data 160 describing the state of thedoor 62 is stored in step 724 along with the image data 250, forexample. Finally, in step 726, the door state information is passed to asecurity control system 238.

FIG. 5C shows a similar process in which an in-field optical signal froma water/humidity sensor 466 is monitored and detected in step 728. Theinformation from the water/humidity sensor 466 is decoded in step 730.In step 732, the humidity information is stored as meta data 160 withthe image data 250 in an image data storage system 174, 310. Finally, instep 734, the humidity information is passed to an environmental controlsystem 242 and possibly stored in a log file within the image datastorage system 174, 310 or used as part of the control algorithm forthat environmental control system 242.

FIG. 5D shows a similar process in which an in-field optical signal froma merchandise sensor 460 is monitored and detected. The steps of theprocess of FIG. 5D are described with reference to an example consumershopping event occurring in the scene of FIG. 2, where an individualselects product 92-8 from shelf 98.

With reference to the scene of FIG. 2, an individual 60-1 selectsproduct 92-8 from shelf 98, for example. A merchandise sensor 460-1detects this removal of the product 92-8. In response, the transducer482 of the merchandise sensor 466 communicates merchandise signals suchas aisle/shelf number, a code associated with product 92-8, and possiblytime stamp information, in examples. The transducer 482 communicatesthis information by accordingly modulating the light of the diode array450 as visible communications.

At the same time, camera4 103-4 captures image data 250 of the scene.Objects within the scene captured within the image data 250 include theindividual 60-1, the shelf 98, and the in-scene merchandise sensor460-1. The image data 250 also includes the visible communications (i.e.modulated merchandise signals) from the diode array 450 of themerchandise sensor 460-1 during the selection of the product 92-8 by theindividual 60-1.

Returning to the method of FIG. 5D, in step 710, an analytics system312,176 analyzes image data 250 captured from surveillance camera 103-4.Here, the image data 250 includes objects within the scene near theproduct shelf 98 and the encoded visible information (e.g. merchandisesignals) sent from the merchandise sensor 460-1.

The analytics system 312,176 then detects the in-field opticalmerchandise signals from the merchandise sensor 460 within the imagedata, in step 736. The in-field merchandise signals are decoded in step738. In step 740, the in-field merchandise signals are stored asmerchandise meta data 160 along with the image data 250 to an image datastorage system 174, 310. Finally, in step 742, the merchandise meta data160 is passed to a business logic system 240 for logistics and analysispurposes. In examples, the merchandise meta data 160 is used by thebusiness logic system 240 for restocking/replenishment purposes and totrack sales of the products 92.

FIG. 5E shows a similar process in which an in-field audio signal froman audio sensor 466 such as a security microphone is monitored anddetected. The steps of the process of FIG. 5E are described withreference to an example event involving an irate customer 60 near apoint of sale terminal 72 in the scene of FIG. 1.

With reference to the scene of FIG. 1, an irate individual 60-1 speaksloudly and in a threatening manner towards a sales person at the pointof sale terminal 72. A microphone 458 of an audio/voice sensor 456detects speech patterns from the individual 60-1 that are indicative ofthreatening behavior. In response, in one example, the transducer 482 ofthe audio/voice sensor 456 communicates security signals such as pointof sale terminal number, a code associated with the potentiallythreatening speech, and possibly time stamp information, in examples.The transducer 482 communicates this information by accordinglymodulating the light of the diode array 450 as visible communications.

At the same time, camera2 103-2 captures image data 250 of the scene.Objects within the scene captured within the image data 250 include theindividual 60, the point of sale terminal 72, and the in-sceneaudio/voice sensor 456. The image data 250 can also include the visiblecommunications (i.e. modulated security signals) from the diode array450 of the audio/voice sensor 456 during the irate display of behaviorby the individual 60.

Returning to the method of FIG. 5E, in step 710, an analytics system312,176 analyzes image data 250 captured from surveillance camera 103-2.Here, the image data 250 includes objects within the scene such as thepoint of sale terminal 72, the irate individual 60, the salesperson, andthe encoded visible information (e.g. audio signals associated withirate customer 60) sent from the in-field audio/ voice sensor 456.

The analytics system 312,176 then detects the in-field optical securitysignals from the audio/voice sensor 456 within the image data, in step744. The in-field security signals are decoded in step 746. In step 748,the in-field security signals are stored as security meta data 160 alongwith the image data 250 to an image data storage system 174, 310.Finally, in step 750, security state information of the metadata 160 ispassed to a security control system 238.

It is also important to note that the surveillance cameras 103 inconjunction with the sensors 462 452, 456, 76 can encode informationwithin the image data 250 for later detection and analysis. For example,during the capturing of image data 250 of a scene, an integrated cameraanalytics system 176 of the camera 103 can detect and “pre-analyze” thevisible information sent from the sensors. In response, the camera 103can include (e.g. encode) additional information within the image data250 for subsequent decoding.

In one example, because the cameras 103 are accessible over the localnetwork 103 but the sensors typically are not, an installer can sendupdated information for a specific high value product over the localnetwork 210 to the surveillance cameras 103. In the example, anincorrect Stock Keeping Unit (SKU) number was originally assigned to theproduct and is associated with a different, low-value product. Then,during capturing of image data 250, if an integrated camera analyticssystem 176 of the camera 103 detects visible information sent from amerchandise sensor 460 that indicates that the high-value product isselected, the camera analytics system 176 can encode additionalinformation (e.g. meta data 160) within the image data 250 in response.For example, the camera analytics system 176 can include the updatedStock Keeping Unit (SKU) number within the meta data 160 with of theimage data 250. Subsequent analytics systems 312 can then read the metadata 160 to adjust the analysis of the image data in response. In thisway, the system 10 is made more flexible and can better proactivelyrespond to unforeseen conditions and/or limitations.

While this invention has been particularly shown and described withreferences to preferred embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the scope of the inventionencompassed by the appended claims.

What is claimed is:
 1. A system for monitoring sensors, the systemcomprising: surveillance cameras generating image data of scenes;sensors in the scenes that detect information concerning the scenes andencode the information so that it can be derived from the image data;and an analytics system analyzing the image data and decoding theinformation from the sensors.
 2. A system as claimed in claim 1, whereinthe sensors detect environmental information and encode theenvironmental information.
 3. A system as claimed in claim 1, whereinthe sensors detect security information and encode the securityinformation.
 4. A system as claimed in claim 1, wherein the sensorsdetect business information and encode the business information.
 5. Asystem as claimed in claim 1, further comprising an image data storagesystem for storing the image data, wherein the analytics systemgenerates meta data from the decoded information and stores the metadata with the image data to the image data storage system.
 6. A systemas claimed in claim 1, wherein the sensors comprise a modulated lightsource that is modulated to encode the information.
 7. A system asclaimed in claim 6, wherein the modulated light source generatesmodulated light in the infrared spectrum.
 8. A system as claimed inclaim 6, wherein the modulated light source is modulated at less than aframe rate of the surveillance cameras.
 9. A system as claimed in claim6, wherein the modulated light source is modulated at less than half theframe rate of the surveillance cameras.
 10. A system as claimed in claim1, further comprising a security control system that receives thedecoded sensor information and generates security alarms.
 11. A systemas claimed in claim 1, further comprising a business logic system thatreceives the decoded sensor information and updates product availabilityinformation.
 12. A system as claimed in claim 1, further comprising anenvironmental control system that receives the decoded sensorinformation and controls environmental systems.
 13. A monitoring method,comprising: generating image data of scenes; detecting informationconcerning the scenes and encoding the information so that it can bederived from the image data; and analyzing the image data and decodingthe information
 14. A method as claimed in claim 13, wherein detectinginformation comprises detecting environmental information and encodingthe environmental information.
 15. A method as claimed in claim 13,wherein detecting information comprises detecting security informationand encoding the security information.
 16. A method as claimed in claim13, wherein detecting information comprises business information andencoding the business information.
 17. A method as claimed in claim 13,further comprising generating meta data from the decoded information andstoring the meta data with the image data.
 18. A method as claimed inclaim 13, further comprising encoding the information by modulating alight source.
 19. A method as claimed in claim 18, wherein the lightsource generates modulated light in the infrared spectrum.
 20. A methodas claimed in claim 18, wherein the light source is modulated at lessthan a frame rate of surveillance cameras that generated the image data.21. A method as claimed in claim 18, wherein the light source ismodulated at less than half of a frame rate of the surveillance cameras.22. A method as claimed in claim 18, further comprising a securitycontrol system receiving the decoded information and generating securityalarms.
 23. A method as claimed in claim 18, further comprising abusiness logic system receiving the decoded information and updatingproduct availability information.
 24. A method as claimed in claim 18,further comprising an environmental control system receiving the decodedsensor information and controlling environmental systems.
 25. A methodof analyzing image data from a surveillance camera, the methodcomprising: installing mechanisms for generating predetermined opticalpatterns in response to events of interest in a scene monitored by thesurveillance camera; monitoring image data for the predetermined opticalpatterns; and generating metadata for the image data in response todetecting the predetermined optical patterns.